#packages 

for (x in c("dplyr", "ggplot2","readxl","plotly"
            ,"psych", "ggfortify")) {
  library(x, character.only = TRUE)
}

Macroeconomic variables

datos<- read_excel("/Users/Juan Alfredo.DESKTOP-ETJ31JC/Documents/Universidad/Termino 2020 2S/R Samples/Pagina/PIB Argentina_1.xlsx")

dt<- datos[,c(2,4,10,13)]

dt %>% mutate(m= import/PIB)  %>% mutate(x= export/PIB)
## # A tibble: 114 x 6
##        PIB import export ITCRM     m     x
##      <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
##  1 391657. 55833. 45376.    NA 0.143 0.116
##  2 437818. 55469. 51528.    NA 0.127 0.118
##  3 439218. 64247. 48417.    NA 0.146 0.110
##  4 443720. 69336. 47327.    NA 0.156 0.107
##  5 421660. 75389. 47985.    NA 0.179 0.114
##  6 466065. 71189. 58228.    NA 0.153 0.125
##  7 458808. 74677. 57721.    NA 0.163 0.126
##  8 465820. 75378. 58182.    NA 0.162 0.125
##  9 430752. 74600. 62896.    NA 0.173 0.146
## 10 449080. 61634. 78021.    NA 0.137 0.174
## # ... with 104 more rows
colnames(datos)
##  [1] "PIB_93"        "PIB"           "import_93"     "import"       
##  [5] "consum_priv93" "consump_priv"  "consum_pub93"  "consump_pub"  
##  [9] "export_93"     "export"        "FBK_93"        "fbkf"         
## [13] "ITCRM"         "PI_export"     "PI_Import"
fechas<- c("1993-01-01", "2021-06-20")
st<- as.Date(fechas[1])
end<- as.Date(fechas[2])


time<- seq(st, end, by= "quarter")
# Create a xts object

datos<- as.ts(datos)

Gross Domestic Product (GDP) of Argentina 1993-2021

p1<- datos %>% ggplot(aes(x=time, y= PIB)) + 
  geom_line(linetype = "dashed", color= "blue") + 
  theme( axis.ticks = element_blank(),
         panel.grid = element_blank(),
         panel.background = element_blank()) + 
  labs(title = "Gross Domestic Product (GDP)", 
       subtitle = "1993-2021", 
       caption = "Source: Indec")  + scale_y_log10()
ggplotly(p1)

Imports

p2<- datos %>% ggplot(aes(x=time, y= import)) + 
  geom_line(linetype = "dashed", color= "brown") + 
  theme( axis.ticks = element_blank(),
         panel.grid = element_blank(),
         panel.background = element_blank()) + 
  labs(title = "Imports ", 
       subtitle = "1993-2021", 
       caption = "Source: Indec")  + scale_y_log10()
ggplotly(p2)

Public consumption

p3<- datos %>% ggplot(aes(x=time, y=consump_pub )) + 
  geom_line(linetype = "dashed", color= "brown") + 
  theme( axis.ticks = element_blank(),
         panel.grid = element_blank(),
         panel.background = element_blank()) + 
  labs(title = "Public consumption ", 
       subtitle = "1993-2021", 
       caption = "Source: Indec")  + scale_y_log10()
ggplotly(p3)

Relationship between Imports & Real Exchange Rate

m is the ratio of imports to GDP.

x is the ratio of export to (domestic) GDP

dt1<- dt %>% mutate(m= import/PIB)  %>% mutate(x= export/PIB) 
p4<- dt1 %>% ggplot(., aes(x= ITCRM, y= m)) + geom_point()+
  geom_smooth(method = "lm") + scale_y_log10() + scale_x_log10()
ggplotly(p4)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 18 rows containing non-finite values (stat_smooth).

Relationship between Exports & Real Exchange Rate

p5<- dt1 %>% ggplot(., aes(x= ITCRM, y= x)) + geom_point()+
  geom_smooth(method = "lm") + scale_y_log10() + scale_x_log10()
ggplotly(p5)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 18 rows containing non-finite values (stat_smooth).

Correlation plot

corPlot(dt, cex = 1.2)